A Framework for Self-configuration in Manufacturing Production Systems

Since a few years, an abundant literature has been published in order to proof the existence of chaotic behaviors both in the field of science and in the field of technique. Until now very few articles studied the conduct of manufacturing production systems. Apparently some production systems let us think that their behavior might be chaotic. Nevertheless, in our opinion, the proof of existing chaos in the production systems has not been totally confirmed. The works presented in this article are aimed to make obvious and to prove the existence of chaotic behaviors in manufacturing production systems. After the presentation of the interest of this study in a manufacturing production environment, we present our analysis method of the dynamic of non-planned production systems. We then justify the choices which have been made regarding in particular: the sub-system in which our study is made, the variable of interest (temporal average of the number of parts in a waiting line), the determinism of the system parameters, and the imposed balance conditions (in the sense that the number of parts is finished regardless of the considered instant). In the second part are presented the results obtained with two manufacturing systems, both very simple and very similar, although they give very different results. We then compare the results with the rules of assignment and management of different waiting lines. In the last part, we show that an actual system, under certain management conditions, can also present a chaotic behavior. This study has been realized from the modeling of a flexible assembly cell.

The paper deals with the situation of manufacturing of customized engineering products under intense competitive environment. Such customized engineering products can be manufactured by different types of production systems such as job production, batch production, mass production, and flexible manufacturing systems. It then becomes an important decision to choose the best one among these production systems which is also dependent on multiple criteria. The three basic criteria predominantly considered in the context of manufacturing are cost, quality, and flexibility. The paper applies the efficient interpretive ranking process (IRP) as an interpretive multi-criteria ranking method to demonstrate its utility in this context. It considers the design of the decision problem by treating the cross-interaction matrix of ‘Alternatives × Criteria’ as a unit matrix, i.e. each alternative is linked with each criterion. In this type of decision problem, the IRP method is implemented with transitive dominance relationships. Further, it carries out the sensitivity analysis concerning different ordinal weights of various criteria. The paper also makes a methodological contribution in IRP by way of computing dominance index rather than simply considering net dominance (which would be either positive or nil or negative). The dominance index gives a better depiction of the dominance of one alternative over the others.

NASA's Visualization, Exploration, and Data Analysis (VEDA) project is an open-source science cyberinfrastructure for data processing, visualization, exploration, and geographic information systems (GIS) capabilities (https://www.earthdata.nasa.gov/esds/veda). VEDA was an ambitious platform and one that was only made possible in the past year by building upon existing NASA projects. The extensive technology community at NASA continues to come together to design, build and use VEDA’s interoperable APIs and datasets.This presentation will demo the current capabilities of VEDA and discuss how these capabilities were designed and architected with the central goals of science delivery, reproducible science, and interoperability to support re-use of data and APIs across NASA’s Earth Science ecosystem of tools. The presentation will close with VEDA’s future plans. In 2023, VEDA will support NASA’s Transform to Open Science (TOPS) program and open-source science initiatives through data, APIs and analytics platforms. In 2023 and beyond, VEDA will advance the state of the art in cloud-based Earth science as well as strengthening the ties of technology within NASA.The projects behind VEDA’s current features are:The Multi-Mission Algorithm and Analysis Platform (https://maap-project.org/, presented at EGU 2019): Recognizing the numerous advantages of open, reproducible science, NASA and ESA are working together to create the Joint ESA-NASA MAAP. The MAAP brings together relevant data and algorithms in a common virtual environment in order to support the global aboveground terrestrial carbon dynamics research community.  The COVID-19 Earth Observation Dashboard (https://www.earthdata.nasa.gov/covid19/): Following the interest in this dashboard, NASA invested in the design and development of a new dashboard infrastructure. This infrastructure is highly configurable to support easily adding new datasets and discoveries. UI and config layers are built upon the VEDA STAC catalog and Cloud-Optimized GeoTIFFs. The Earthdata Information Systems (EIS) pilots (https://eis.smce.nasa.gov/): Scientists at NASA worked together on open science tools to develop new research projects using Earth Observation data across the domains of fire, freshwater, greenhouse gasses, and sea level rise. ArcGIS Enterprise in the Cloud (gis.earthdata.nasa.gov) provides GIS capabilities. The projects listed above have all made VEDA a reality in a year. The scientists from EIS are using the new dashboard infrastructure to tell their stories and the analytics backend from MAAP to scale their science.In 2023, VEDA plans many initiatives in the work to extend its reach within and beyond NASA. There are many advanced technologies at NASA and we see an opportunity for VEDA to support closing the information gaps across groups. For example, VEDA will support driving standards for using, publishing and visualizing NASA’s Earthdata Zarr archives and also deliver interoperable APIs for its data stores to support dynamic data visualization and storytelling.VEDA will also extend its reach beyond NASA by providing a JupyterHub for any user to explore the data behind NASA Earth Science, specifically the discoveries presented in the Earthdata Dashboard.

The design of production and logistic systems is a process of managing both technical and organisational variants in order to identify the best solution for a given system. This paper discusses design issues of production systems that are applied to an internal logistic system in the automotive industry. As far as the production systems design (PSD) is concerned, three basic classes of software tools usually pertain: computer-aided design (CAD), process simulation, and information systems. However, these software tools have been used with low levels of integration. Vik et al. (2010b, 2010c) proposed integrating these software resources in production systems and developed an advanced tool called integrated design of systems (IDS). The proposed IDS tool involves a wide set of functions for the most common tasks of PSD, from conceptualisation to implementation, including systems analysis (P-Q, cluster, and material flow analysis), automatic generation of simulation models, generation of alternatives for the layout of facilities and factories, material flows display, transportation system design, and iterative buffer size specification. The IDS approach takes advantage of simulation, CAD systems, and their integration. This paper will demonstrate the concept and functionalities of the proposed tool in a real industrial case study.

With the rapid developments in ICT, the current agriculture businesses have become increasingly data-driven and are supported by advanced data analytics techniques. In this context, several studies have investigated the adopted data analytics platforms in the agricultural sector. However, the main characteristics and overall findings on these platforms are scattered over the various studies, and to the best of our knowledge, there has been no attempt yet to systematically synthesize the features and obstacles of the adopted data analytics platforms. This article presents the results of an in-depth systematic literature review (SLR) that has explicitly focused on the domains of the platforms, the stakeholders, the objectives, the adopted technologies, the data properties and the obstacles. According to the year-wise analysis, it is found that no relevant primary study between 2010 and 2013 was found. This implies that the research of data analytics in agricultural sectors is a popular topic from recent years, so the results from before 2010 are likely less relevant. In total, 535 papers published from 2010 to 2020 were retrieved using both automatic and manual search strategies, among which 45 journal articles were selected for further analysis. From these primary studies, 33 features and 34 different obstacles were identified. The identified features and obstacles help characterize the different data analytics platforms and pave the way for further research. 2022 The Author(s)

Data and analytics are indispensable parts of any successful business. Despite that, they can lead to serious privacy issues if not used safely. Even though the financial and legal consequences of privacy breaches are profound, the most damaging one, in the long run, is customer distrust. This is particularly the case for hypersensitive data such as personal health information. In recent years, the significance of privacy, security, and trust to the design of successful modern systems are being recognized more than ever before. Nevertheless, the current state of data management systems is far from achieving this goal. Particularly, in medical data management systems, handling sensitive data, mistrust is a barrier to many core processes such as data collection, sharing and analytics. Consequently, it is a primary cause of suboptimal medical services in our time. The primary direction of this thesis is to respond to these concerns of ethical nature by taking data privacy as a fundamental human right into serious consideration in the design of these processes. We highlight the emerging role of the blockchain in health information systems and recognize blockchain's ample opportunities for trust establishment. Consequently, we propose solutions based on this novel technology. More precisely, this thesis proposes a privacy-preserving data market for incentivedriven data collection for the development of reliable mathematical models that accelerate medical research. The blockchain aspect of the platform provides transparency as to the purpose for which the data is used to avoid the perception of data misuse/abuse. Secondly, this thesis proposes flexible blockchain-based access control mechanisms with multiple privacy protection layers for user-centric data sharing. Furthermore, it proposes a distributed machine learning-as-a-service platform for data analytics whose computational integrity can be verified efficiently by users. The strength of this platform lies in mutual privacy protection of user data and service provider intellectual property.

Development of Automated Flexible Manufacturing System for Medium Variation and Medium Volume Production

Process industries represent a significant share of the European industry in terms of energy consumption and environmental impact. In this area, data analytics techniques can prove effective when applied to the optimization of production processes and can lead to significant savings, both economic and environmental. However, application of these techniques is not straightforward. In many cases, process industries must invest in the monitoring and data integration as well as in the development and maintenance of the underlying infrastructure for data analytics, often capable of handling big data. In this talk, we present the architecture of a cross-sectorial Big Data platform for the process industries. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of large volumes of data from multiple industry domains. Such platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment enabling the data scientists to build these functions and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic moulding area.

Automated and systematic testing of automation systems (AS) and production systems (PS) require an integrated testing tool chain for test case development, execution and reporting. In practice, the test automation tool chain cannot be fully automated because of missing links between different tools used in the test automation process. Closing these gaps typically require (high) human effort. Furthermore, domain and software testing expertise is often bundled by one (expensive) engineer who is responsible for the application domain (reflected in use cases and test cases) and software tests (software test code). This paper presents a flexible Testing Automation Framework (TAF) that enables the configuration of test processes involving different tools and various layers for test automation and enables separated roles for the application domain and software tests. We build on best-practice test automation from Software Engineering and design a test automation process for the automation systems domain. We demonstrate the feasibility with a use case, derived from production systems automation, with selected tools covering all test automation layers. First results showed the feasibility of the framework in the evaluation use case making test processes more flexible and automated. Although the successful implementation of the TAF can support the efficient configuration and execution of test processes, there is additional effort for preparing the flexible and automated tool chain.

This paper explores the development of a conceptual model for Data-Driven Customer Value Management (CVM) aimed at enhancing product lifecycle performance and market penetration. The proposed model integrates customer insights, data analytics, and product lifecycle management to create a comprehensive framework for maximizing customer value throughout the product journey. It emphasizes the importance of data collection, personalization, segmentation, and cross-functional collaboration in driving customer engagement, loyalty, and retention. By leveraging advanced technologies such as CRM systems, machine learning, and analytics platforms, businesses can gain actionable insights to optimize product offerings, marketing strategies, and customer interactions. The study highlights how businesses can align customer value strategies with each stage of the product lifecycle—from development to decline—ensuring sustained relevance and competitiveness in the market. Furthermore, the paper provides practical recommendations for businesses to enhance product lifecycle performance and improve market penetration by focusing on customer-centric strategies, data-driven decision-making, and the optimization of communication channels. The findings underscore the need for businesses to continuously monitor and refine their CVM strategies based on customer feedback and data insights. Future research could explore the integration of emerging technologies and industry-specific adaptations further to enhance the effectiveness of the proposed conceptual model.

Geoprocessing is a set of tools that can be used to efficiently address several pressing chal-lenges for the global economy ranging from agricultural productivity, the design of transport networks, to the prediction of climate change and natural disasters. This paper describes an Open Source Framework developed, within three European projects, for Ena-bling High-Performance Computing (HPC) and Cloud geoprocessing services applied to agricultural challenges. The main goals of the European Union projects EUXDAT (EUro-pean e-infrastructure for eXtreme Data Analytics in sustainable developmenT), CYBELE (fostering precision agriculture and livestock farming through secure access to large-scale HPC-enabled virtual industrial experimentation environment empowering scalable big data analytics), and EOPEN (opEn interOperable Platform for unified access and analysis of Earth observatioN data) are to enable the use of large HPC systems, as well as big data management, user-friendly access and visualization of results. In addition, these projects focus on the development of software frameworks, and fuse Earth-observation data, such as Copernicus data, with non-Earth-observation data, such as weather, environmental and social media information. In this paper, we describe the agroclimatic-zones pilot used to validate the framework. Finally, performance metrics collected during the execution (up to 182 times speedup with 256 MPI processes) of the pilot are presented.

This paper describes the architecture of a cross-sectorial Big Data platform for the process industry domain. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of data from multiple industry domains. Such a platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment with which to build these functions, and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. During the development, we adopted a user-centered approach to gather requirements from different stakeholders which were used to design architectural models from different viewpoints, from contextual to deployment. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic molding industry.

The location analytics industry has the potential to stimulate critical sociological discussions concerning the credibility of data analytics to enact new spatial classifications and metrics of socio-economic phenomena. Key debates in the sociology of geodemographics are revisited in this article in light of recent developments in algorithmic culture to understand how location analytics impacts the structural contexts of classification and relevance in digital marketing. It situates this within a locative imaginary, where marketers are experimenting with consolidating the epistemes of behavioural targeting, classification and performance evaluation in urban environments through spatial analytics of movement. This opens up future research into the political and cultural economies of relevance in media landscapes and the social shaping of valuable subjects by third-party data brokers and analytics platforms that have become matters of public and regulatory concern.

Chapter 9 - Agent-Based Control of Production Systems—and Its Architectural Challenges

In the current context of societal development, the digitalization of educational processes is undergoing rapid transformations. The aim of this article is to analyze the possibilities of integrating digital tools into the management activities of general secondary education institutions. The study uses theoretical developments from Ukrainian and foreign researchers, the analysis of regulatory documents, as well as a practical review of digital platforms and systems that can be used in the management of general secondary education institutions. The article outlines the main tasks for the educational institution and its leaders in creating a digital educational environment. Based on the analysis of scientific works by researchers, the following actions are suggested to adapt to the digitalization of education. The article provides examples of automated systems for monitoring educational quality, platforms for distance learning, feedback tools, and data analytics. Special attention is given to digital services for improving the qualifications of pedagogical staff and organizing internal electronic document circulation. The necessity of maintaining the institution's website is also emphasized, with examples of website builders provided. Successful digital transformation in the management activities of a general secondary education institution requires a systematic approach, the creation of a digital development strategy for the institution, the use of proven digital platforms, the enhancement of digital competence among all participants in the educational process, and ensuring information security. The inclusiveness, flexibility, and effectiveness of digital tools will contribute to the improvement of the quality of education and management processes. The introduction of digital technologies should be accompanied by continuous assessment of their effectiveness and adaptation to the specific needs of the educational institution